Intelligent Moving Target Recognition Based on Compressed Seismic Measurements and Deep Neural Networks

Abstract

Moving target recognition is a critical task for a variety of applications, ranging from environmental monitoring to regional security protection. Recently, many deep learning (DL) methods have been proposed to recognize the seismic features of moving targets. However, the established DL algorithms are mainly challenged by time-consuming feature extraction and lack of robustness. In this article, a novel moving target recognition method [Compression Observation-Seismic DL (CO-SDL)] is proposed to solve the above two problems simultaneously. CO-SDL first uses a measurement matrix to project the seismic signal onto a compressed domain and obtain compressed seismic measurements. This operation removes redundant data while retaining valuable seismic information and suppressing noise energy. Following that, CO-SDL efficiently and stably extracts deep nonlinear features from compressed seismic measurements and then accurately classifies the feature vectors. To evaluate the proposed method, a comprehensive seismic dataset is developed. This dataset covers six types of common moving targets, and the SNR ranges of all signal types are greater than 15 dB. The proposed method and the benchmark methods are tested on this dataset. Experimental results prove that the presented CO-SDL method is ten times faster than the state-of-the-art methods with comparable accuracy. Furthermore, the CO-SDL method shows the strongest robustness.